• Real-Time State and Parameter Estimation for Dynamic Inversion-Based Control of a Quadcopter (2025)

​This project presents an integrated estimation and control framework for quadcopters operating under partially known or incorrect dynamic parameters. A dynamic inversion-based controller is designed to achieve precise trajectory tracking, relying on accurate state and parameter estimates. Two Extended Kalman Filter (EKF) models are implemented: a constant
velocity model for full-state estimation, and a full-order EKF that estimates all system states and uncertain parameters such as mass. The full-order EKF demonstrates strong estimation and control performance even in the presence of parameter inaccuracies, while the constant velocity model performs well in estimation but provides limited closed-loop control performance due to state unobservability. Simulation results confirm that the proposed EKF-integrated control framework enables robust quadcopter operation despite modeling uncertainties.

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• Dynamic Inversion-Based Fault-Tolerant Control of Quadcopter With a Propeller Failure (2024)

​This project addresses the problem of quadcopter control in the case of sudden propeller failure. A dynamic inversion-based control architecture is used to develop a controller that maintains the quadcopter’s stability along with the trajectory tracking in such a scenario. The method presented in the paper involves a two-loop control structure. The inner loop prioritizes the quadcopter’s stability while compromising the yaw stability to the uniform spin about the yaw axis. On the other hand, the outer loop helps in trajectory tracking by a quadcopter while one of its propellers fails. This approach highlights the possibilities of Fault-Tolerant Control to be implemented in the unmanned aerial vehicle.

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• Input-Output Feedback Linearization Control Design of Longitudinal Flight Dynamics of Aircraft  (2024)

​This project presents the design and implementation of an Input-Output Feedback Linearization (IOFL) controller for the longitudinal flight dynamics of an aircraft. The controller stabilizes the inherently unstable system by addressing the challenges posed by non-minimum phase behavior through the introduction of an additional output, enabling effective linearization and control. The study evaluates the system's performance under nominal and off-nominal conditions, demonstrating enhanced stability, precise output decoupling, and improved tracking accuracy for critical parameters such as velocity, flight path angle, and pitch angle. Robustness analysis under varying controller gains and pitch reference biases confirms the system's ability to achieve bounded steady-state errors, ensuring reliable performance in practical aerospace scenarios. The results validate the suitability of IOFL for advanced aerospace control applications, providing a foundation for further research in robust and adaptive control methodologies.

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• Optimal Control of a Spacecraft Performing Orbital Transfer (2023)                                                       

​This project focuses on the optimal control of a spacecraft performing orbital transfer, with an emphasis on developing energy-efficient trajectories and control strategies for satellite transitions between co-planar orbits. A state feedback control system was designed to compensate for deviations from the nominal trajectory caused by perturbations, ensuring precise maneuvering during the transfer. To address practical challenges such as measurement and process noise, a Linear Quadratic Gaussian (LQG) controller was implemented, incorporating an extended Kalman filter for accurate state estimation. The proposed approach demonstrates improved trajectory optimization and robust control performance, offering practical solutions for efficient and reliable satellite orbital transfers.

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• Attitude Estimation Using Rate Gyros and Angle Measurements (2023)

​This project involves the successful implementation of a continuous-discrete Kalman filter to estimate critical parameters essential for dynamic systems, including Euler angles, angular velocities, scale factors, and sensor biases. A comprehensive sensitivity analysis of noise covariance parameters was conducted, offering valuable insights into the filter's responsiveness and adaptability to variations in measurement noise. The results demonstrated consistent convergence of Euler angle errors within the 3-sigma bound, confirming the filter's reliability and robustness for accurate parameter estimation. This work underscores the effectiveness of the Kalman filter in enhancing precision and stability in dynamic system state estimation.​

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• Automatic Landing Autopilot (2022)

​This project entails the design and simulation of an automatic landing autopilot system, addressing the critical need for all-weather aircraft operations. The system guides the aircraft along a pre-determined glide slope and initiates a controlled "flare" maneuver at a specific altitude to ensure a smooth transition from descent to touchdown with a low descent rate. The project focuses on the longitudinal dynamics of a jet transport aircraft in a landing configuration, employing linearized dynamics and state-space modeling. Simulations validate the system's performance, demonstrating precise glide-slope tracking and effective flare execution, ensuring safe and reliable landing operations under diverse conditions. This work highlights the potential of automation to advance the safety and efficiency of modern aviation.​

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• Mars Rover: Proof of Life (2022)                                                                                                                                             

​This senior design project involved the development of the "Proof of Life Module," a vital subsystem integrated into an existing automated rover aimed at the scientific exploration of extraterrestrial environments. Collaborating with a team of five, the project focused on designing a module capable of excavating soil samples, performing chemical analyses, and detecting potential signs of life. Autonomous control algorithms were implemented and optimized using Arduino to enhance the rover's operational efficiency and navigation precision. The project demonstrates a seamless combination of innovative engineering and automation to advance robotic exploration capabilities in challenging extraterrestrial terrains.​

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• Supersonic Airfoil Design (2021)

This project explores the design optimization of a supersonic airfoil to achieve the maximum lift-to-drag ratio under specified constraints. Utilizing shock expansion theory, the aerodynamic performance of various airfoil geometries was evaluated. A MATLAB-based computational tool was developed to automate the performance analysis based on user-defined airfoil dimensions. The optimization process identified feasible airfoil designs by systematically varying geometric parameters, ensuring compliance with design constraints such as maintaining a minimum thickness of 10% of the chord length. The final design, selected for its superior lift-to-drag ratio, revealed the critical influence of airfoil thickness and asymmetry on performance. Parametric studies further highlighted the nonlinear relationships between geometric variables and aerodynamic efficiency, providing insights into the design considerations for high-performance supersonic airfoils.​​